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PHYSICS PHD DISSERTATION DEFENSE: Joe DeRose

Ph.D. Candidate:  Joe DeRose

Research Advisor:  Risa Wechsler

Date: Thursday, June 27th, 2019
Time: 10:00 am
Location: Varian 355

Title: Cosmological Simulations for Precision Measurement of Dark Energy with Wide Field Galaxy Surveys

Abstract:
Current and upcoming cosmological surveys are turning their eyes towards the late time universe in an attempt to answer fundamental questions about its composition and the physical laws that govern it. Given the non-linear nature of the observables studied by these surveys, perturbative modeling approaches have limited faculty. Thus, non-perturbative simulation approaches are necessary in order to connect theory with observation and harness the statistical power contained in the deluge of data that will be collected in the coming years. In this thesis defense, I will discuss a number of efforts, all centered around using simulations to interpret data and aid analyses with wide field galaxy surveys.

The first part of this talk will focus on a model for galaxy formation based on the evolution of dark matter halos and subhalos in high resolution N-body simulations, and its extension to large volume, low resolution lightcone simulations. I will then show the application of this model to the Dark Energy Survey (DES), the most precise weak lensing survey to date. In particular, I will present the suite of simulations that I created with this model and show that it agrees well with the first year of DES (DES Y1) data. I will then show how this suite was used to test the analysis choices made in the DES Y1 analysis of weak lensing and galaxy clustering.

Finally, I will discuss an effort to use cosmological simulations as models for highly non-linear observables in galaxy surveys. First I will present a well validated suite of simulations suitable for this type of modeling, which efficiently spans a large cosmological parameter space. I will then overview the models that we built from these simulations, and the outlook for applying them to upcoming data in order to use it to its fullest capacity.

June 27, 2019 - 10:00am
Varian 355